Consider a multiple hypothesis testing setting involving rare/weak effects: relatively few tests, out of possibly many, deviate from their null hypothesis behavior. Summarizing the significance of each test by a P-value, we construct a global test against the null using the Higher Criticism (HC) statistics of these P-values. We calibrate the rare/weak model using parameters controlling the asymptotic distribution of non-null P-values near zero. We derive a region in the parameter space where the HC test is asymptotically powerless. Our derivation involves very different tools than previously used to show the powerlessness of HC, relying on properties of the empirical processes underlying HC. In particular, our result applies to situations where HC is not asymptotically optimal, or when the asymptotically detectable region of the parameter space is unknown.
翻译:考虑一种涉及稀有/微弱效应的多重假设测试设置:相对较少的测试,在可能很多的测试中,偏离了它们的无效假设行为。用P值来概括每次测试的重要性,我们用这些P值的高级批评性统计数据来构建对无效物的全球测试。我们使用控制非核P值无症状分布的参数来校准稀有/弱度模型,我们从参数空间中得出一个区域,在参数空间中, HC 测试在时间上是无能为力的。我们的衍生过程涉及非常不同的工具,不同于以往用来显示HC 的无能性的工具,依靠HC 基础的经验性过程的特性。特别是,我们的结果适用于HC 不具有无症状性最佳性的情况,或者当参数空间的无症状性可探测区域未知时。